Manufacturability analysis is classified in two categories. The first category is to exclusively use design rule checking (DRC) rules and operations to check a design against manufacturing rules. The second category is to use models-based analysis to perform manufacturability analysis using models.
Either category of analysis can be used for yield prediction of random or systematic defects in manufacturability analysis. Many different approaches are available to determine the distribution of defects and to calculate the yield. Various impacts of the defects to the design can be determined to identify locations where design modifications can significantly improve the yield. By determining such locations of high impact, design changes at a relatively small number of locations can be used to improve the yield.
However, the DRC approach is often not sufficiently accurate, especially for designs that do not have Manhattan geometries. The DRC approach is computationally expensive if performed for high levels of accuracy. The models-based approach is generally more accurate but also computationally more expensive and slower to perform.
Thus, there is a need for techniques that allow performance optimization of computationally expensive model analysis techniques.